Designation D6982 − 09 (Reapproved 2016) Standard Practice for Detecting Hot Spots Using Point Net (Grid) Search Patterns1 This standard is issued under the fixed designation D6982; the number immedia[.]
Trang 1Designation: D6982−09 (Reapproved 2016)
Standard Practice for
This standard is issued under the fixed designation D6982; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision A number in parentheses indicates the year of last reapproval A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1 Scope
1.1 This practice provides equations and nomographs, and a
reference to a computer program, for calculating probabilities
of detecting hot spots (that is, localized areas of soil or
groundwater contamination) using point-net (that is, grid)
search patterns Hot spots, more generally referred to as targets,
are presumed to be invisible on the ground surface Hot spots
may include former surface impoundments and waste disposal
pits, as well as contaminant plumes in ground water or the
vadose zone
1.2 For purposes of calculating detection probabilities, hot
spots or buried contaminants are presumed to be elliptically
shaped when projected vertically to the ground surface, and
search patterns are square, rectangular, or rhombic
Assump-tions about the size and shape of suspected hot spots are the
primary limitations of this practice, and must be judged by
historical information A further limitation is that hot spot
boundaries are usually not clear and distinct
1.3 In general, this practice should not be used in lieu of
surface geophysical methods for detecting buried objects,
including underground utilities, where such buried objects can
be detected by these methods (see GuideD6429)
1.4 Search sampling would normally be conducted during
preliminary investigations of hazardous waste sites or
hazard-ous waste management facilities (see GuideD5730) Sampling
may be conducted by drilling or by direct-push methods In
contrast, guidance on sampling for the purpose of making
statistical inferences about population characteristics (for
example, contaminant concentrations) can be found in Guide
D6311
1.5 This standard does not purport to address all of the
safety concerns, if any, associated with its use It is the
responsibility of the user of this standard to establish
appro-priate safety and health practices and determine the
applica-bility of regulatory limitations prior to use.
2 Referenced Documents
2.1 ASTM Standards:2
D5730Guide for Site Characterization for Environmental Purposes With Emphasis on Soil, Rock, the Vadose Zone and Groundwater(Withdrawn 2013)3
D6051Guide for Composite Sampling and Field Subsam-pling for Environmental Waste Management Activities D6311Guide for Generation of Environmental Data Related
to Waste Management Activities: Selection and Optimiza-tion of Sampling Design
D6429Guide for Selecting Surface Geophysical Methods
3 Terminology
3.1 Definitions:
3.1.1 hot spot—a localized area of soil or groundwater
contamination
3.1.1.1 Discussion—A hot spot may be considered as a
discrete volume of buried waste or contaminated soil where the concentration of a contaminant of interest exceeds some prespecified threshold value Although hot spots are more likely to have variable sizes and shapes and not have clear and distinct boundaries, ellipitically shaped hot spots or targets with well defined edges are assumed for the purposes of calculating detection probabilities The assumption that hot spots have elliptical shapes is not inconsistent with known historical patterns of contaminant distribution
3.1.2 sampling density—the number of soil borings (that is,
sampling points) per unit area
3.1.3 semi-major axis, a—one-half the length of the long
axis of an ellipse For a circle, this distance is simply the radius
3.1.4 semi-minor axis, b—one-half the length of the short
axis of an ellipse
3.1.5 target—the object or “hot spot” that is being searched
for
1 This practice is under the jurisdiction of ASTM Committee D34 on Waste
Management and is the direct responsibility of Subcommittee D34.01.01 on
Planning for Sampling.
Current edition approved May 1, 2016 Published May 2016 Originally
approved in 2003 Last previous edition approved in 2009 as D6982 – 09 DOI:
10.1520/D6982-16.
2 For referenced ASTM standards, visit the ASTM website, www.astm.org, or contact ASTM Customer Service at service@astm.org For Annual Book of ASTM Standards volume information, refer to the standard’s Document Summary page on the ASTM website.
3 The last approved version of this historical standard is referenced on www.astm.org.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959 United States
Trang 23.1.6 threshold concentration—the concentration of a
con-taminant above which a hot spot is considered to be detected
3.1.7 unit cell—the smallest area into which a grid can be
divided so that these areas have the same shape, size and
orientation For a triangular grid, the unit cell is a 60°/120°
rhombus comprised of two equilateral triangles with a common
side
3.2 Symbols: a = length of the semi-major axis of an ellipse
b = length of the semi-minor axis of an ellipse
A T = area of target or hot spot For an ellipse, A T = πab.
A S= search area
S = the “shape” of an elliptical target (that is, the ratio of the
length of the semi-minor axis to the length of the semi-major
axis of an ellipse, b/a)
G = the distance between nearest grid nodes of a unit cell
Q = the ratio of the length of the long side of a rectangular
grid cell to the length of the short side
A C = the area of the unit cell For a square, A sq = G2 For a
rectangle A re = Q·G2 For a 60°/120° rhombus, A rh = [(√3)/
2]G2 The inverse of A Cis the sampling density
β= the probability of not detecting a hot spot
P(hit) = probability of detection (that is, 1 − β)
4 Significance and Use
4.1 Search sampling strategies have found wide utility in
geologic exploration where drilling is required to detect
subsurface mineral deposit, such as when drilling for oil and
gas Using such strategies to search for buried wastes and
subsurface contaminants, including volatile organic
compounds, is a logical extension of these strategies
4.2 Systematic sampling strategies are often the most
cost-effective method for searching for hot spots
4.3 This practice may be used to determine the risk of
missing a hot spot of specified size and shape given a specified
sampling pattern and sampling density
4.4 This practice may be used to determine the smallest hot
spot that can be detected with a specified probability and given
sampling density
4.5 This practice may be used to select the optimum grid
sampling strategy (that is, sampling pattern and density) for a
specified risk of not detecting a hot spot
4.6 By using the algorithms given in this practice, one can
balance the cost of sampling versus the risk of missing a hot
spot
4.7 Search sampling patterns may also be used to optimize
the locations of additional ground water monitoring wells or
vadose zone monitoring devices
5 Assumptions
5.1 One or more targets or hot spots exist and are equally
likely to occur in any part of the search area
5.2 When projected vertically upward to a level ground
surface, the target appears as an ellipse or a circle (Fig 1) The
probable size and shape of a hot spot can only be guessed from
past site or facility records, known layout of the site or facility,
and personal knowledge
5.3 The search pattern is either a square, a rectangular, or an equilateral triangular grid Borings are made at the intersec-tions of grid lines (that is, nodes) (Fig 2)
5.4 Borings or direct-push devices are directed downward vertically and the detection of the target is unambiguous Such
an assumption presumes that the full length of a boring would
be subject to analysis as contiguous intervals of the boring If sampling intervals are discontinuous, then contamination might be missed if it occurred between sampled intervals If sampling intervals are too long, then a hot spot may not be detected because of dilution of a hot spot with less contami-nated portions of the sampled interval The criteria for detec-tion of contaminants may be prespecified threshold concentra-tions (for example, screening levels) that would trigger further investigation of sites or facilities
5.5 The area of the borehole or direct-push device is infinitely small compared to the target area The algorithms used in this practice assume that boreholes or direct-push devices have no area, but rather are vertical lines projected downward from grid nodes
6 Preliminary Considerations
6.1 Before designing a hot spot detection strategy, a pre-liminary investigation of the area containing possible hot spots
or targets should be conducted From historical records, physi-cal layout of buildings and equipment, known transportation pathways, landscape features, and eyewitness accounts, one may be able to identify areas with a high probability of subsurface contamination Areas with different expected prob-abilities of detection of a hot spot or other target should be clearly mapped
6.2 Within areas of relatively uniform expected probability
of hot spot or target detection, sampling grids of prespecified
grid spacing G and type (for example, square, rectangular, or
triangular) may be overlain Areas with smaller hot spots
FIG 1 Projection of Boundaries of Subsurface Contamination to
the Ground Surface
Trang 3should have correspondingly higher sampling densities
com-pared to areas with large hot spots However, areas with greater
hazard from missing a hot spot should also have
correspond-ingly higher sampling densities than areas with a lesser hazard
Ideally, the starting point for each grid and its orientation
should be randomly determined
6.3 When searching for hot spots, threshold concentrations
for detection may be established by a regulatory authority
Whether or not a threshold concentration is exceeded will
depend upon the physical distribution of the contaminant, the
volume of the sampling device, the sampling intervals selected,
and the sensitivity of the analysis If contamination occurs in a
discrete layer, then the probability of detecting a hot spot will
decrease with increasing volume of material sampled in a bore
hole or if the sampling interval exceeds the depth of the
discrete hot spot layer The analytically determined
contami-nant concentration may then be less than the threshold
concen-tration because of the dilution of the hot spot layer with
uncontaminated layers of soil or waste Further, a hot spot
confined to a discrete layer may be missed entirely by not
sampling that layer For this reason, continuous sampling is
recommended
6.4 Detection of contaminant levels in samples above
threshold concentrations may trigger more detailed sampling to
better define the spatial extent of hot spots or buried
contami-nation Again, a grid sampling strategy will be the most
efficient
7 Determining Hot Spot Detection Probabilities
7.1 Case I—If the longest dimension of an elliptical target is
less than or equal to the grid spacing (that is, 2a ≤ G), then the
target can only be hit once and the probability P of detecting
the hot spot is simply equal to the ratio of the area of the target
A T to the area of the unit cell A C (that is, P = A T /A C)
7.2 Case 2—If the longest dimension of an elliptical target
is greater than the grid spacing (that is, 2a > G), then the target
may be hit more than once In this case, algorithms developed
by Singer and Wickman ( 1 )4employing affine transformations
and programmed in FORTRAN by Singer ( 2 ) are required to
calculate the exact probability of detecting the target This
program is limited to ellipses having a shape S between 0.05 and 1.0 and the ratio a/G between 0.05 and 1.0 Singer’s
algorithms have been adapted by J R Davidson ( 3 ) to the
personal computer (PC) running under the MS DOS operating system Supporting documentation for this program, ELIPGRID-PC, is available from Oak Ridge National
Labora-tory ( 4 , 5 ).
7.3 Randomly Oriented Elliptical Target—The probability
of detecting a target, P(hit), of a specified size a shape S and for
a specified grid G spacing can be obtained from nomographs
shown inFigs 3 and 4for square and equilateral triangular grid sampling patterns, respectively Data for these nomographs were generated using the ELIPGRID-PC program To use these
graphs, first calculate the ratio a/G Then draw a vertical line from the point represented by the ratio a/G on the x-axis of the
graph to the curve representing the prespecified shape of the
ellipse Then draw a horizontal line to the y-axis For shapes
other than those shown on the graphs, one must interpolate
4 The boldface numbers in parentheses refer to the list of references at the end of this standard.
FIG 2 Grid Patterns for Detecting Hot Spots Borings are Made at the Grid Nodes
Trang 4between curves with closest values of S The value on the
y-axis represents the probability of at least one hit of the target.
Using these same graphs, one can also determine the required
grid spacing to detect an elliptical target of shape at a prespecified probability of detection In this case, draw a horizontal line from the prespecified probability of a hit to the
FIG 3 Nomograph Relating the Probability of Detecting a Single Hot Spot to the Ratio a/G for Selected Shapes (b/a)
Using a Square Grid with Grid Spacing G.
FIG 4 Nomograph Relating the Probability of Detecting a Single Hot Spot to the Ratio a/G for Selected Shapes (b/a)
Using a Triangular Grid with Grid Spacing G.
Trang 5curve representing the prespecified shape of the ellipse Then
draw a vertical line down to the x-axis From the ratio a/G at
the point of intersection with the x-axis, one can determine the
minimum required grid spacing Similarly, one can also
deter-mine the smallest sized hot spot of a given shape that can be
detected for a given grid spacing and probability of detection
by calculating a from the ratio a/G and grid spacing G.
Alternatively, one can use the computer program
ELIPGRID-PC
7.4 Oriented Elliptical Target—If the orientation of the
elliptical target with respect to the grid lines is specified, then
the probability of detecting the target must be determined using
the computer program ELIPGRID-PC
8 Comparing the Relative Efficiencies of Search Patterns
8.1 The efficiency of a search pattern is measured as the
probability that a target (for example, hot spot) will be hit at
least once Given the same sampling density, a sampling
pattern with a higher probability of hitting a target will be more
efficient than a sampling pattern with a lower probability of
hitting the same target The relative efficiency, RE, of one
sampling pattern over another when searching for a target is
measured as the percent difference in the efficiency of two
equivalent density sampling patterns For example, RE =
100 % (P TRI − P SQR )/P SQR where P TRI and P SQR are the
probabilities of detecting a target with an equilateral triangular
grid and a square grid, respectively By extension, for the same
probability of detecting a target, a more efficient sampling
pattern will require fewer borings, and will thus be more
economical In this section, the relative efficiencies of hitting
randomly oriented (that is, orientation unknown) and oriented
elliptical targets of prespecified size and shape are compared
using different sampling patterns having equivalent sampling
densities
8.2 Randomly Oriented Elliptical Targets:
8.2.1 Square versus Equilaterial Triangular Grid—When
the criterion for detection is one or more hits, the equilateral
triangular grid is up to 6 % more efficient than the square grid
(for a circular target where a/G = 0.55) while a square grid is
never more than 0.2 % more efficient ( 6 ) Efficiencies are the
same for a/G ratios less than 0.5 since a target can be hit no
more than once For a/G > 0.5 and if two or more hits are
required for detection, then a square grid is overall more
efficient than an equilateral triangular grid
8.2.2 Point-net versus Random—When one or more hits is
required for detection, then point-net search sampling
strate-gies are more efficient than random sampling stratestrate-gies for
detecting subsurface contamination This can be easily shown
by comparing the probability of detecting a hot spot using a
grid sampling approach to the probability of detecting a hot
spot by random sampling (see Appendix X1) Where two or
more hits are required for detection and a/G < 0.5, then a
random search is more efficient ( 6 ).
8.3 Targets with Known Orientation:
8.3.1 Square versus Equilateral Triangular Grid—When
one or more hits is required for detection, an equilateral
triangular grid is generally more efficient when the angle of
orientation is close to 30°, 90°, or 150° whereas a square grid
is more efficient when the angle of orientation is between 25° and 65° or between 115° and 155° Between 25° and 35°, efficiencies are nearly the same These orientations minimize the probabilities of hitting the same target more than once which would result in less efficient sampling
8.3.2 Rhombic Grid versus Square and Equilateral
Trian-gular Grids—A rhombus is a parallelogram having opposite
sides equal in length A rhombus is also a square if the inside angles are 90° Two equilateral triangles having a common side become a rhombus with inside angles of 60° and 120° If the angle of orientation of an elliptical target is known, then it has been shown that a rhombic search pattern is optimal if the long diagonal of the rhombus is oriented parallel to the long axis of
the elliptical target ( 7 ). Table 1 gives multiplication factors necessary to calculate the lengths of the diagonals of a rhombic grid for a desired probability of detecting an elliptical target of
known size and shape ( 8 ) For a given probability of detection
p, the optimum diagonal distances are d1= 2a·f1(p) and d2=
2b·f2(p).
8.3.3 Example 1—If there exists an elliptical target of known orientation with major axis (2a) of length 200 ft and minor axis (2 b) of length 100 ft, what are the optimum lengths
of the diagonals of a rhombic grid that would yield a 90 % probability of detecting this elliptical target? FromTable 1, d1
= 200·f1(0.90) = 200·1.73867 = 347.7 ft and d2= 100·f2(0.90)
= 100·1.00382 = 100.3 ft
9 Computing the Number of Borings and Grid Spacings for Specified Costs
9.1 Costs for conducting a search for hot spots can be roughly split between the cost of mobilization and
demobili-zation C Mand a cost of each boring and associated laboratory
or field analysis, C S , times the number of borings, n The total cost would equal C M + nC S With a known budget, C T, and an
estimate for C M and C S, one can determine the number of
borings that can be taken for a given area of coverage, A S First calculate the number of borings,n5 C T 2C M
C S The required area
of the unit cell A C would then be equal to A S /n The appropriate grid spacing G can then be determined from the formula given
for the different types of unit cells under the terminology section Using these formulae, it can be shown that for the
same sampling density, the grid spacing G for an equilateral
triangular grid is slightly larger than that for a square grid by a
TABLE 1 Multiplication Factors to Calculate the Optimum Lengths of the Diagonals of a Rhombic Grid Oriented such that the Long Axis of the Elliptical Target is Parallel to the Longest
Diagonal of the Rhombic Grid
0.05 7.37658 4.25888 0.55 2.22411 1.28409 0.10 5.21605 3.01148 0.60 2.12943 1.22943 0.15 4.25888 2.45886 0.65 2.04590 1.18120 0.20 3.68828 2.12943 0.70 1.97148 1.13823 0.25 3.29890 1.90462 0.75 1.90462 1.09964 0.30 3.01148 1.73868 0.80 1.84415 1.06472 0.35 2.78810 1.60971 0.85 1.78907 1.03292 0.40 2.60801 1.50574 0.90 1.73867 1.00382 0.45 2.45886 1.41962 0.95 1.67320 0.96602 0.50 2.33267 1.34676 1.00 1.50000 0.86602
Trang 6factor of =2/=351.0746.
9.2 Example 2—If a hot spot search was budgeted for
$50,000 with the cost for mobilization and demobilization set
at $25,000 and a per boring cost set at $1000 and the area to be
searched is 10 000 m2(one hectare), (1) what is the maximum
number of borings that can be made, and (2) what grid spacing
would be required for a square grid and for an equilateral
triangular grid? The number of samples n would be
$50,0002$25,000
$1000 525. The unit cell size A C would be 10 000
m2/25 = 400 m2 For a square grid, the grid spacing G would
be =A C5=400520m. For an equilateral triangular grid, the
grid spacing G would beŒ2A C
=3521.49m. For a given G, one
can then use the nomographs to determine the probability of
detecting a hot spot of a specified size a and shape S Testing
various values for A C and S will reveal that an equilateral
triangular grid is more efficient than a square grid for most
values of a and S.
10 Probability of Detection with Multiple Hot Spots
10.1 The probability of detecting a hot spot can easily be
extended to two or more hot spots if the number of hot spots is
known, it is assumed that each hot spot has an equal probability
of being detected, and the locations of the hot spots are
independent of one-another Because the probabilities of
de-tection are assumed to be independent and equal, one can take
advantage of the binomial probability distribution, b~x;n;P!
5~x!P x~12P!n2x which gives the probability of x successes (that
is, hits) in n independent trials (that is, targets) with probability
of success P The quantity, ~ x!,the number of combinations of
n distinct hot spots taken x at a time, is equivalent to n!
x!~n2x!!.
The advantages of using the binomial formula are that the
following probabilities are easily determined: (1) the
probabil-ity of detecting exactly x hot spots out of a total number of n
hot spots (2) the probability of not detecting any of the hot
spots (that is, P(no hit) = (1 − p) n ), and (3) the probability of
detecting at least one hot spot out of a total of n hot spots (that
is, P(hit) = 1 − (1 − p) n)
10.2 Example 3—If it is known that three identical hot spots
are located randomly and independently within a search area and each has a probability of detection of 50 % for any search pattern, what is the probability of not detecting any of these hot
spots? Since the number of hits, x, is 0 and n = 3, b~x;n;P!
5~0!0.50 0~0.50!3 50.125. The probability of hitting one, two or three hot spots can be similarly determined by appropriate substitution
11 Effect of Composite Sampling and Sampling Interval
on Hot-Spot Detection
11.1 Where the cost of analysis is high relative to the cost of sampling, it may be more economically advantageous to composite soil or waste samples The same grid patterns would
be used as previously described However, individual samples would be composited from nearest neighbor boring locations
In composite sampling, samples to be composited should have the same size, shape and orientation If the soil or waste material is horizontally layered, or will be removed in layers, compositing over similar soil horizons or layers may be most appropriate Where contaminants of interest have vapor pres-sures that would result in loss of contaminant if exposed to the atmosphere, samples should not be composited and care should
be taken to avoid losses by volatilization during sampling and shipping Please refer to GuideD6051for additional guidance
on composite sampling
11.2 The threshold concentration for hot-spot detection would necessarily be lower for a composite sample given that individual (component) sample concentrations will have been physically averaged In the most conservative approach, the threshold concentration for composite samples would be equal
to the threshold concentration for single samples divided by the number of samples comprising the composite sample
12 Keywords
12.1 preliminary investigation; sampling; site investigation; soil investigation; subsurface exploration; systematic sampling
APPENDIX
(Nonmandatory Information) X1 COMPARING THE EFFICIENCY OF A POINT-NET SEARCH PATTERN TO A RANDOM SEARCH FOR DETECTING
EL-LIPTICAL HOT SPOTS
X1.1 The question often arises as to whether a random
search or a systematic sampling pattern is the most efficient
method for detecting targets This section demonstrates that
where only one hit is required for detection of a randomly
oriented target that systematic sampling is more efficient For
samples obtained at random within a defined search area, the
probability of hitting a target can be calculated from the binomial distribution as:
P~r!5 n !
~n 2 r!!r ! p
r~1 2 p!~n2r! (X1.1)
Trang 7where p is the proportion of the search area, A S, occupied by
the target; r is the number of hits; and n is the total number of
samples taken However, since this equation would have to be
evaluated for all possible number of hits, it is simpler to solve
this equation for the probability of no hits:
P~0!5 n !
~n 2 0!!0 !p
0~1 2 p!~n20! (X1.2)
P~0!5~1 2 p!n where P(0) is the “consumer’s risk” for a random sampling
pattern The complementary probability of one or more hits is
therefore:
P~hit!51 2 P~0!5 1 2~1 2 p!n
(X1.3)
Since p = A T /A S where A Tis the area of the target, it follows
that:
P~hit!51 2 P~0!5 1 2~1 2 A T /A S!n (X1.4)
X1.2 It can easily be shown that random sampling is less
efficient than a square grid sampling design for detecting an
elliptical hot spot For an elliptical target, the proportion p of
the total search area occupied by the target is:
p 5 A T
A S5
πab
where πab is the area of the elliptical target and A S is the
search area, and a and b are the semi-major and semi-minor
axes of the ellipse, respectively Since the area of the square
grid (D2) equals A S /n:
p 5 πab
Since the shape of an ellipse S = b/a:
p 5π
nSa
DD2
One can now directly compare the probability of detecting
an elliptical target using a random search versus a systematic
search for different values of a, S, and D.
X1.2.1 For comparable sampling densities, as the size of the circular target increases relative to the grid spacing, the probability of missing the target decreases more rapidly for the square grid sampling pattern than for the random sampling pattern Further, given the same sampling densities, the prob-ability of missing a target increases with increasing size of the search area
X1.2.2 It was noted by Singer ( 6 ), however, that if two or
more hits on a single target are desirable for detection, then random sampling may be more efficient as the length of the semi-major axis increases relative to the grid spacing
REFERENCES (1) Singer, D A., and Wickman, F E., “Probability Tables for Locating
Elliptical Targets with Square, Rectangular and Hexagonal
Point-nets,” Special Bulletin 1-69, Mineral Sciences Experimental Station,
The Pennsylvania State University, 1969, p 100.
(2) Singer, D A., “Elipgrid, a FORTRAN IV Program for Calculating the
Probability of Success in Locating Elliptical Targets with Square,
Rectangular and Hexagonal Grids,” Geocom Programs, Vol 4, 1972,
pp 1-16.
(3) Davidson, J R.,“ELIPGRID-PC: Upgrade Version,”
ORNL/TM-13103, Oak Ridge National Laboratory, Oak Ridge Tennessee,
De-cember 1995.
(4) Davidson, J R., Jr., “ELIPGRID-PC: Hot Spot Probability
Calculations,” Battelle/Pacific Northwest National Laboratory,
Richland, Washington, 1995 The program can be downloaded from
http://dqo.pnl.gov/software/elipgrid.htm.
(5) Davidson, J R.“Monte Carlo Tests of the ELIPGRID-PC Algorithm,”
ORNL/TM-12899 , Oak Ridge National Laboratory, Oak Ridge
Tennessee, 1995.
(6) Singer, D A., “Relative Efficiencies of Square and Triangular Grids in
the Search for Elliptically Shaped Resource Targets,” J of Research, U.S Geological Survey, Vol 3, No 2, 1975, pp 163-167.
(7) Drew, L J., “Pattern Drilling Exploration: Optimum Pattern Types
and Hole Spacings When Searching for Elliptical Targets,” Math-ematical Geology, Vol 11, No 2, 1979, pp 223-254.
(8) Mickey, M R., Jr., and Jespersen, H W., Jr., “Some Statistical
Problems of Uranium Exploration, Final Technical Report,”
RME-3105, United States Atomic Energy Commission, September 8, 1954
.
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